• Title, Summary, Keyword: Rotating machine

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Rejection Study of Mearest Meighbor Classifier for Diagnosis of Rotating Machine Fault (회전기계 고장 진단을 위한 최근접 이웃 분류기의 기각 전략)

  • 최영일;박광호;기창두
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.81-84
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    • 2000
  • Rotating machine is used extensively and plays important roles in the industrial field. Therefore when rotating machine get out of order, it is necessary to know reasons then deal with the troubles immediately. So many studies far diagnosis of rotating machine are being done. However by this time most of study has an interest in gaining a high recognition But without considering error $rate^{(1)(2)(3)}$ , it is not desirable enough to apply h the actual application system. If the manager of system receives the result misjudging the condition of rotating machine and takes measures, we would lose heavily. So in order to play the creditable diagnosis, we must consider error rate. T h ~ t is. it must be able to reject the result of misjudgment. This study uses nearest neighbor classifier for diagnosis of rotating $machine^{(4)(8)}$ And the Smith's rejection $method^{(1)}$ used to recognize handwritten charter is done. Consequently creditable diagnosis of rotating machine is proposed.

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Design, Development and Analysis of Embedded Systems for Condition Monitoring of Rotating Machines using FFT Algorithm

  • Dessai, Sanket;Naaz, Zakiyaunnissa Alias Naziya
    • Journal of international Conference on Electrical Machines and Systems
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    • v.3 no.4
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    • pp.428-432
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    • 2014
  • Rotating machines are an integral part of large electrical power machinery in most of the industries. Any degradation or outages in the rotating electric machinery can result in significant losses in productivity. It is critical to monitor the equipment for any degradation's so that it can serve as an early warning for adequate maintenance activities and repair. Prior research and field studies have indicated that the rotating machines have a particular type of signal structure during the initial start-up transient. A machine performance can be studied based on the effect of degradation in signal parameters. In this paper a data-acquisition system and the FFT algorithm has been design and model using the MATLAB and Simulink. The implementation had been carried out on the TMS320 DSP Processor and various testing and verification of the machine performance had been carried out. The results show good agreement with expected results for both simulated and real-time data. The real-time data from AC water pumps which have rotating motors built-in were collected and analysed. The FFT algorithm provides frequency response and based on this frequency response performance of the machine had been measured.The FFT algorithm provides only approximation about the machine performances.

Seismic Anslysis of Rotating Machine-Foundation System (회전기계-기초의 상호작용을 고려한 지진해석)

    • Journal of the Earthquake Engineering Society of Korea
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    • v.2 no.2
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    • pp.1-12
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    • 1998
  • The seismic behaviour of rotating machine-foundation systems subjected to six-component nonstationary earthquake ground accelerations is analyzed. The rotating machine-foundation system is idealized by using discs, rotating shaft, fluid-film journal bearings, pedestals, and space frame foundation. Thus, governing equations of motion for the rotating machine-foundation system are obtained by considering Gyroscopic effect, Coriolis effect, dynamic characteristics of fluid-film journal bearings, and translational and rotational motions of seismic rigid base. The influences due to Gyroscopic effects, Coriolis effects, and rotational motions of seismic base on the overall structural response are demonstrated by a numerical example. The results show that the inclusion of base rotations and Gyroscopic effects contributes significantly to the system response.

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Technology Roadmap for Rotating Machine Diagnosis (회전기 진단기술 지도)

  • Lee, Dong-Keun;Kim, Hyeon-Il;Oh, Bong-Keun;Lee, Kwang-Ho
    • Proceedings of the KIEE Conference
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    • pp.7-9
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    • 2005
  • The rotating machine diagnosis technology is very important techniques to secure reliability of facility operation and life extension of operation to rotating machines that are exposed in danger of the insulation deterioration. The rotating machine diagnosis technology road map minimizes economic losses according to the unpredictable accidents of the rotating machine diagnosis technology. As technology is secured, it strengthen the competitiveness and the diagnosis technology road maps will be realizing the technique independence and applying industrial sites

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A Study on the Establishment of Insulation Diagnosis Cycle for High Voltage Rotating Machine (고압회전기 절연진단 주기 설정에 관한 연구)

  • Lee, Young-Jun;Kim, Hee-Dong
    • Proceedings of the KIEE Conference
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    • pp.1939-1941
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    • 2000
  • Nondestructive and destructuve insulation tests were performed the high voltage rotating machine in the local thermal power plants. Nondestructive tests include measurements of insulation resistance. polarization index, AC current. tan$\delta$, partial discharge. Destructive tests include measurements of AC hipot and DC hipot. This paper propose to establish the insulation diagnosis cycle for high voltage rotating machine.

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Performance Characteristics of an Inductively Coupled Magnetic Probe Developed for Off-line Monitoring of a Rotating Machine (발전기 정지중 진단을 위하여 개발된 유도결합 마그네틱 프로브의 성능특성)

  • Park, Noh-Joon;Yang, Sang-Hyun;Kong, Tae-Sik;Kim, Hee-Dong;Park, Dae-Hee
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • pp.46-46
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    • 2010
  • In order to detect exact corona discharge point at stator winding of a rotating machine, an inductively coupled magnetic probe has been developed, which consists of U-shaped and truncated manganese ferrite inductor as a helix. The measured current intensity is somewhat higher than commercially developed probe. It has been shown that the measured intensity of proposed probe is suitable for manual localization as to off-line stator winding monitoring of rotating machine.

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Core Material Design of a High Performance Rotating Machine Considering Magnetic Anisotropy

  • Ikariga Atsushi;Enokizono Masato;Shimoji Hiroyasu;Yamashiro Hirofumi
    • KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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    • v.5B no.3
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    • pp.248-252
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    • 2005
  • This paper deals with a new design method for a small-size rotating machine with high power. In order to achieve high performance, secondary excitation by Nd-Fe-B magnets and the grain oriented electrical steel sheets were selected and a new design using dual rotors is proposed. The outline of the high-performance rotating machine will be presented and the results of the finite element analysis by using this method combined with the E&SS modeling will be shown in the paper.

Basic Properties of Micropump with Magnetic Micromachine

  • Hisatomi, Shinichi;Yamazaki, Aya;Ishiyama, Kazushi;Sendoh, Masahiko;Yabukami, Shin;Agatsuma, Shigeto;Morooka, Keiko;Arai, Ken Ichi
    • Journal of Magnetics
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    • v.12 no.2
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    • pp.84-88
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    • 2007
  • A micropump with spiral-type magnetic micromachine was fabricated. When a rotating magnetic field was applied, the machine rotated and pumped a surrounding liquid. We experimentally examined the basic properties of this pump. We found that the pressure and the flow rate could be controlled by the rotating frequency, and this pump could work under wide range kinematic viscosity. In addition, we proposed a disposable pump system using the machine. When a plate installed a fluid channel and the machine was set on a stage for generating a rotating magnetic field, the machine worked as the pump.

A Comparison of Artificial Neural Networks and Statistical Pattern Recognition Methods for Rotation Machine Condition Classification (회전기계 고장 진단에 적용한 인공 신경회로망과 통계적 패턴 인식 기법의 비교 연구)

  • Kim, Chang-Gu;Park, Kwang-Ho;Kee, Chang-Doo
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.12
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    • pp.119-125
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    • 1999
  • This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

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A study on the fault diagnosis of rotating machine by machine learning (기계학습을 적용한 회전체 고장진단에 관한 연구)

  • Jeon, Hang-Kyu;Kim, Ji-Sun;Kim, Bong-Ju;Kim, Won-Jin
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.4
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    • pp.263-269
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    • 2020
  • In this study, a rotating machine that can reproduce normal condition and 8 fault conditions were produced, and vibration data was acquired. Feature is calculated from the acquired data, and accuracy is analyzed through fault diagnosis using artificial neural networks and genetic algorithms. In order to achieve optimal timing and higher accuracy, features by three domains were applied to the fault diagnosis. The learning number was selected as a setting variable. As a result of the rotating machine fault diagnosis, high precision was found in the frequency domain than in others, and precise fault diagnoses were accomplished through all of 10 operations, at the learning number of 5000 and 8000. Given the efficiency of time, it was estimated to be the most efficient when the number of learning was 5000.